Data Analyst Roadmap
Roadmap to Data Analyst
Data Analyst and Data Scientist are the buzzwords of the present day. We have seen a sudden inclination towards this career path in recent times. A Data Analyst job role has countless benefits, most notably its compensation and that's one of the many reasons why everyone is so excited about it. But data analysis is not a piece of cake and has its own struggles. So, before you get swayed away by its better side, do a fact check and make sure this role is a right fit for you.
Since its an emerging role, there is no fixed curriculum to follow about. At present, there are heaps of online paid courses and bootcamps out there assuring to make you a job-ready data analyst. But honestly, I think you can become a data analyst without spending a dime. All you need is to devote 3-4hrs daily and a stable internet connection. To help you on your journey, here I am penning down a brief blog on who exactly is a data analyst and more importantly how to become one on your own without much expense.
PS: While certificates have their own place to prove your skills, completing a course just for the sake of a certificate is not going to help you at all. So whatever courses you take up, please make sure that you learn, practice and acquire that skill.
Who is a Data Analyst
“In modern times, data holds all the answers but the main thing is asking the right questions!!!”.
In simple terms, a data analyst’s role is to collect, process and perform analysis of large datasets.
Humongous volume of data is generated in this age which now has a term, Big Data. This data can be in the form of customer feedback, accounts, logistics, marketing research, and so on. A data analyst takes this data and figures out numerous measures, such as how to improve customer experience, price new materials, and how to reduce transportation costs, to name a few.
To put it in another words, a Data Analyst is the one who turns this raw data into information in order to draw out meaningful, actionable insights. These insights are then used to help businesses make smart decisions. And these insights are then used by the companies in many ways ranging from forming marketing strategies to making improvements in the production process.
Note: All the resources mentioned here are my personal suggestions and you can cover the topics from any resources accessible to you.
Statistics
The foremost skill required to become a good data analyst is Statistics. Statistical skills are crucial to interpret data correctly. Basic topics like Types of data, Basic chart types, Aggregation of data, Variation of data should be covered and 1 week will be sufficient for this. Beginner level expertise in statistics is enough.
Resources:
Khan academy: https://www.khanacademy.org/math/statistics-probability
Khan academy YouTube: https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Statistics by Marin : https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa
Statquest YouTube channel: https://www.youtube.com/user/joshstarmer
Advanced Excel
Microsoft Excel is till date the most prominent data crunching tool in the industry. Excel is not just a spreadsheet but a boon that provides substantial functionalities required to structure data conveniently and hassle-free.
Features such as data filters, functions, formulas, Charts and plots, Pivot table, vlookup and VBA macros should be covered in one week to do data analysis. Beginner to Advanced level expertise of Excel is required for a good data analyst.
Resources:
Datacamp course: https://learn.datacamp.com/skill-tracks/spreadsheet-fundamentals?version=1
Chandoo’s youtube channel: https://www.youtube.com/channel/UC8uU_wruBMHeeRma49dtZKA
SQL
There is no doubt that data plays a very key role in the life of a Data Analyst and hence you need to be proficient in Data management which includes Data extraction, transformation and Loading. SQL is a great tool that runs queries through which you can manipulate data by performing tasks like storing the data, reading the data, creation of the new table, and deletion of the older data or the garbage. Topics like Joins, Unions, Order by and Group by should be covered. Hence, intermediate level expertise in SQL is necessary.
Resources:
kudavenkat playlist (first 16): https://www.youtube.com/playlist?list=PL08903FB7ACA1C2FB
khanacademy SQL course: https://www.khanacademy.org/computing/computer-programming/sql
BI Tools
Data visualization helps in conveying your story in simple words so that everybody can understand. Week 3 and 4 can be spent in learning BI Tools for data visualization. Data visualization enables you to find patterns in the data by which you can create a good story to present to your clients.
Power BI, Tableau and Qlik sense are three most popular tools for this in the industry. However, you can just learn one or two tools and that should be enough to make you a good data analyst. I personally recommend Tableau since I find it more easier and convenient comparatively. Beginner to Intermediate level expertise is required.
Resources:
Simplilearn Tableau course: https://lms.simplilearn.com/courses/2926/Tableau-Training/syllabus
Abhishek Agarwal: https://www.youtube.com/playlist?list=PL6_D9USWkG1C4raCOTlTf_oq4XnNNNtm9
Bharti consultancy: https://www.youtube.com/playlist?list=PLyD1XCIRA3gQmN73dHwQWr4R08ABZFMtZ
Python
Programming has a remarkable position in your journey. Although, both Python and R are widely accepted programming language for data analysis, I would suggest Python because of its readability and easiness to learn.
Basic to Intermediate concepts of Python or any language like Conditionals, Loops, Functions, Read and write etc., should be covered to do effective analysis. If you are already a python programmer, you can skip this and focus on python libraries instead whereas if you are beginner, it will take you around 1–2 weeks to cover these topics.
Resources:
Codebasics python tutorials (first 16) — https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0
Codebasics python HINDI tutorials — https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO
Numpy, Pandas, Matplotlib
These are the libraries provided by Python for data visualization and other purposes. Numpy and pandas are essential for analyzing data whereas matplotlib and seaborn lets you visualize your data. You can learn either Matplotlib or Seaborn as both of them serve the same purpose.
Resources:
Codebasics Numpy playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uset9zIVzJWqplaWBiacTEU
Codebasics pandas playlist (first 9): https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy
Codebasics matplotlib playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu4Lr8_kro2AqaO6CFYgKOl
Codebasics seaborn tutorials: https://www.youtube.com/playlist?list=PLJIOr9Je9wzHT-ptgfelpt2Nyx4VGX_j9
Simplilearn course (cover only chapters of numpy, matplotlib and Pandas): https://lms.simplilearn.com/courses/2772/Data-Science-with-Python/syllabus
Projects, Portfolio and Resume
Now that you have gained all the right skills, you need to showcase your skills and stand out from others to land a job. Projects and Portfolios will do that for you, so invest an ample amount of time in preparing these. You can find enough and more datasets in Kaggle for doing projects. Do a bit research and start working on it. Once its done, prepare an insightful resume and start applying for jobs and internships.
Resources:
Data Analyst Resume Guide- https://www.simplilearn.com/data-analyst-resume-guide-pdf?source=frs_recommended_resource_clicked
Kaggle exploratory data analysis
* Notebooks: https://www.kaggle.com/notebooks
* Datasets: https://www.kaggle.com/dataset
Project ideas:
Alex the Analyst Portfolio Project Series: https://www.youtube.com/watch?v=qfyynHBFOsM&list=PLUaB-1hjhk8H48Pj32z4GZgGWyylqv85f&t=0s
Communication skills — To make others understand your data and findings, you need to present your data in a storytelling format with concrete results and values so that other people can understand what you are saying. Hence, good communication skill is a must for a data analyst.